View source: R/make.sample.correlations.heatmap.R
make.sample.correlations.heatmap | R Documentation |
Make a heatmap of inter-sample correlations of code counts
make.sample.correlations.heatmap(
nano.counts, cor.method = 'pearson', fname.stem = NULL, covs = NULL
)
nano.counts |
A gene by sample matrix or data-frame of NanoString counts, where row names are probe names and column names are sample IDs. Counts can be normalized or raw. |
cor.method |
Which distance method to use for clustering. Accepts 'pearson' (default), 'kendall', or 'spearman' |
fname.stem |
To use in filename. Description of how CNAs were called is suggested. Defaults to NULL |
covs |
A sample by covariate data-frame. Currently only accepts sample covariates 'Type' and 'Cartridge' (see output of |
Make a clustered heatmap of code count correlations for all samples
None
Cindy Yao and Emilie Lalonde
## Not run:
# load data
data(NanoString.DNA.raw);
data(PhenoData);
# make sure raw count data frame has gene names for rownames!
NanoString.DNA.formatted <- NanoString.DNA.raw[, -(1:3)];
rownames(NanoString.DNA.formatted) <- NanoString.DNA.raw$Name;
# plot raw NanoString count correlations
make.sample.correlations.heatmap(
nano.counts = NanoString.DNA.formatted,
covs = PhenoData[, c('SampleID', 'Cartridge', 'Type')]
);
## End(Not run)
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